library(tidyverse)
library(ggplot2)        
library(scales)         
library(ggthemes)       
library(gridExtra)

Source

The diamonds data set is available with the ggplot2 package. It contains approximately 54K observations and 10 variables, which include carat, cut, color, clarity, depth, table, price, x (length in mm), y (width in mm), and z (depth in mm). It is a clean data set, with no missing values.

Data Description

summary(diamonds)

##      carat               cut        color        clarity          depth      
##  Min.   :0.2000   Fair     : 1610   D: 6775   SI1    :13065   Min.   :43.00  
##  1st Qu.:0.4000   Good     : 4906   E: 9797   VS2    :12258   1st Qu.:61.00  
##  Median :0.7000   Very Good:12082   F: 9542   SI2    : 9194   Median :61.80  
##  Mean   :0.7979   Premium  :13791   G:11292   VS1    : 8171   Mean   :61.75  
##  3rd Qu.:1.0400   Ideal    :21551   H: 8304   VVS2   : 5066   3rd Qu.:62.50  
##  Max.   :5.0100                     I: 5422   VVS1   : 3655   Max.   :79.00  
##                                     J: 2808   (Other): 2531                  
##      table           price             x                y         
##  Min.   :43.00   Min.   :  326   Min.   : 0.000   Min.   : 0.000  
##  1st Qu.:56.00   1st Qu.:  950   1st Qu.: 4.710   1st Qu.: 4.720  
##  Median :57.00   Median : 2401   Median : 5.700   Median : 5.710  
##  Mean   :57.46   Mean   : 3933   Mean   : 5.731   Mean   : 5.735  
##  3rd Qu.:59.00   3rd Qu.: 5324   3rd Qu.: 6.540   3rd Qu.: 6.540  
##  Max.   :95.00   Max.   :18823   Max.   :10.740   Max.   :58.900  
##                                                                   
##        z         
##  Min.   : 0.000  
##  1st Qu.: 2.910  
##  Median : 3.530  
##  Mean   : 3.539  
##  3rd Qu.: 4.040  
##  Max.   :31.800  
##

# First 10 lines
head(diamonds, 10)

## # A tibble: 10 × 10
##    carat cut       color clarity depth table price     x     y     z
##    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
##  2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
##  3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
##  4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
##  5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
##  6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
##  7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
##  8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
##  9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
## 10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39

Notes:
Diamond color include 7 types ranging from D (best) to J (worst).
Diamond clarity include 8 types: I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best). In the summary, both IF and I1 (the extremes) are categorized as ‘other’, having approximately 2.5K observations both toegether.
Length in mm as the variable x.
Width in mm as the variable y.
Depth in mm as the variable z.
Small amounts of very expensive pieces drive the mean up. In this case the median is a better measurement variable of the center of the distribution.

1. Exploratory Analysis on Price (max, min, mean, median)

1.1 By cut, color and clarity

diamonds %>% 
  group_by(cut) %>% 
  summarise(max_price = max(price),
            min_price = min(price),
            mean_price = mean(price),
            median_price = median(price),
            count = n())

## # A tibble: 5 × 6
##   cut       max_price min_price mean_price median_price count
##   <ord>         <int>     <int>      <dbl>        <dbl> <int>
## 1 Fair          18574       337      4359.        3282   1610
## 2 Good          18788       327      3929.        3050.  4906
## 3 Very Good     18818       336      3982.        2648  12082
## 4 Premium       18823       326      4584.        3185  13791
## 5 Ideal         18806       326      3458.        1810  21551

diamonds %>% 
  group_by(color) %>% 
  summarise(max_price = max(price),
            min_price = min(price),
            mean_price = mean(price),
            median_price = median(price),
            count = n())

## # A tibble: 7 × 6
##   color max_price min_price mean_price median_price count
##   <ord>     <int>     <int>      <dbl>        <dbl> <int>
## 1 D         18693       357      3170.        1838   6775
## 2 E         18731       326      3077.        1739   9797
## 3 F         18791       342      3725.        2344.  9542
## 4 G         18818       354      3999.        2242  11292
## 5 H         18803       337      4487.        3460   8304
## 6 I         18823       334      5092.        3730   5422
## 7 J         18710       335      5324.        4234   2808

diamonds %>% 
  group_by(clarity) %>% 
  summarise(max_price = max(price),
            min_price = min(price),
            mean_price = mean(price),
            median_price = median(price),
            count = n())

## # A tibble: 8 × 6
##   clarity max_price min_price mean_price median_price count
##   <ord>       <int>     <int>      <dbl>        <dbl> <int>
## 1 I1          18531       345      3924.         3344   741
## 2 SI2         18804       326      5063.         4072  9194
## 3 SI1         18818       326      3996.         2822 13065
## 4 VS2         18823       334      3925.         2054 12258
## 5 VS1         18795       327      3839.         2005  8171
## 6 VVS2        18768       336      3284.         1311  5066
## 7 VVS1        18777       336      2523.         1093  3655
## 8 IF          18806       369      2865.         1080  1790

1.2 Price distribution

ggplot(diamonds, aes(x = price)) +
  geom_histogram(color = 'black', fill = 'SteelBlue', binwidth = 500) +
  scale_x_continuous(labels = dollar, breaks = seq(0, 20000, 1000)) +
  labs(title = 'Distribution of diamonds price',
       x = 'Price', y = 'Count') +
  theme_fivethirtyeight() +
  theme(axis.title = element_text(), axis.text.x = element_text(angle=90))

1.2.1 Zoom in the largest peak of the price distribution

ggplot(diamonds, aes(x = price)) +
  geom_histogram(color = 'black', fill = 'SteelBlue', binwidth = 50) +
  scale_x_continuous(labels = dollar, breaks = seq(0, 2000, 100)) +
  labs(title = 'Price distribution of diamonds - 0-2000$',
       x = 'Price', y = 'Count') +
  coord_cartesian(c(0, 2000)) +
  theme_fivethirtyeight() +
  theme(axis.title = element_text(), axis.text.x = element_text(angle=90))

1.3 Price related prints

How many diamonds are in the price range

Less than $500

diamonds %>% 
  filter(price < 500) %>% 
  summarise(count = n())

## # A tibble: 1 × 1
##   count
##   <int>
## 1  1729

Between $500 and $1000

diamonds %>% 
  filter(price >= 500 & price < 1000) %>% 
  summarise(count = n())

## # A tibble: 1 × 1
##   count
##   <int>
## 1 12770

Between $1000 and $1500

diamonds %>% 
  filter(price >= 1000 & price < 1500) %>% 
  summarise(count = n())

## # A tibble: 1 × 1
##   count
##   <int>
## 1  5511

Between $1500 and $2000

diamonds %>% 
  filter(price >= 1500 & price < 2000) %>% 
  summarise(count = n())

## # A tibble: 1 × 1
##   count
##   <int>
## 1  4193

2. Further analysis on diamond prices

2.1 Price distribution per cut

ggplot(diamonds, aes(x=price)) +
  geom_histogram(color='black', fill='SteelBlue', binwidth = 30) +
  scale_x_continuous(labels=dollar, breaks=seq(0, 5000, 200)) +
  labs(title='Price distribution by cut quality - 0-5000$',
       x = 'Price', y = 'Count') +
  theme_fivethirtyeight() +
  theme(axis.title = element_text(), axis.text.x = element_text(angle=90)) +
  coord_cartesian(c(0, 5000)) +
  facet_grid(cut~.)

qplot(x = price, data=diamonds) +
  facet_wrap(~cut, scales = 'free') +
  labs(x = 'Price', y = 'Count')

2.2 Summaries on the prices per quality cut

cut_price <- data.frame(diamonds$cut, diamonds$price)

fair <- cut_price %>% 
  filter(cut_price$diamonds.cut == 'Fair')
summary(fair$diamonds.price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     337    2050    3282    4359    5206   18574

good <- cut_price %>% 
  filter(cut_price$diamonds.cut == 'Good')
summary(good$diamonds.price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     327    1145    3050    3929    5028   18788

v_good <- cut_price %>% 
  filter(cut_price$diamonds.cut == 'Very Good')
summary(v_good$diamonds.price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     336     912    2648    3982    5373   18818

premium <- cut_price %>% 
  filter(cut_price$diamonds.cut == 'Premium')
summary(premium$diamonds.price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     326    1046    3185    4584    6296   18823

ideal <- cut_price %>% 
  filter(cut_price$diamonds.cut == 'Ideal')
summary(ideal$diamonds.price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     326     878    1810    3458    4678   18806

2.3 Price per carat by cut quality

ggplot(diamonds, aes(x=price/carat)) +
  geom_histogram(color='black', fill='SteelBlue', binwidth=0.05) +
  labs(title='Price/Carat by Cut quality',
       x = 'Price/Carat', y = 'Count') +
  scale_x_log10(breaks = trans_breaks("log10", function(x) 10^x),
                labels = trans_format("log10", math_format(10^.x))) +  
  theme_fivethirtyeight() +
  theme(axis.title = element_text(), axis.text.x = element_text()) +
  facet_grid(cut~., scale = "free")

2.4 Price vs clarity for all cuts

ggplot(diamonds, aes(x=clarity, y=price, fill=cut)) +
  geom_boxplot() +
  labs(title='Price vs Clarity',
       subtitle='per cut',
       x = 'Clarity', y = 'Price') +
  theme_fivethirtyeight() +
  theme(axis.title = element_text(), axis.text.x = element_text())

2.5 Matrix with price vs clarity for all cuts per color

ggplot(diamonds, aes(x=clarity, y=price, fill=cut)) +
  geom_boxplot() +
  labs(x = 'Clarity', y = 'Price',
       title='Distribution of price',
       subtitle='across cut, clarity and color') +
  theme_fivethirtyeight() +
  theme(axis.title = element_text(), axis.text.x = element_text()) +
  facet_grid(color~.)

2.6 Price per carat of diamonds across the different colors

ggplot(diamonds, aes(x=color, y=price/carat, fill=color)) +
  geom_boxplot() +
  labs(title='Distribution of price',
       subsitle='across colors',
       x='Color',
       y='Price per Carat') +
  coord_cartesian(ylim=c(1000, 6000)) +
  scale_y_continuous(labels=dollar)

2.7 Price vs carat with top 1% of both omitted

ggplot(diamonds, aes(x=carat, y=price)) +
  geom_point(alpha=.05) +
  labs(title='Price vs Carat',
       subtitle='Omitted top 1%') +
  scale_x_continuous(limits=c(0, quantile(diamonds$carat, 0.99))) +
  scale_y_continuous(breaks=seq(0, 18000, 2000),
                     limits=c(0, quantile(diamonds$price, 0.99)),
                     labels=dollar) +
  theme_fivethirtyeight() +
  theme(axis.title = element_text(), axis.text.x = element_text()) +
  theme(plot.subtitle = element_text(size=10))

## Warning: Removed 926 rows containing missing values (geom_point).

Takeaways

Cut

The number of Ideal diamonds(21551) is much larger than the others but its average price($3458) its also the lowest;
The number of Fair diamonds(1610) is the lowest and its average price($4359) its the second highest;
The average price for Premium diamonds($4584) is the highest for all cuts;

Color

Color G is the most represented in the data set - 11292 occurrences;
Color J is the least represented in the data set - 2808 occurrences - while still having the highest average price: $5324;
The lowest average price is for the color E: $3077;

Clarity

Clarity of SI1 and VS2 are the most represented with 13065 and 12258 occurrences respectively;
The highest average price belongs to clarity SI2 - $5063 - which is greater than all the others by at least $1000;
I1, SI1, VS2 and VS1 average prices range between $3800-$3900;
The lowest average price belongs to VVS1 with a value of $2523.

3. Interquartile range (IQR)

3.1 For the best and worst colors

diamonds %>% 
  group_by(color) %>% 
  filter(color == 'D') %>% 
  summarise(quartile_25 = quantile(price, 0.25),
            quartile_75 = quantile(price, 0.75),
            IQR = quartile_75 - quartile_25)

## # A tibble: 1 × 4
##   color quartile_25 quartile_75   IQR
##   <ord>       <dbl>       <dbl> <dbl>
## 1 D             911       4214. 3302.

diamonds %>% 
  group_by(color) %>% 
  filter(color == 'J') %>% 
  summarise(quartile_25 = quantile(price, 0.25),
            quartile_75 = quantile(price, 0.75),
            IQR = quartile_75 - quartile_25)

## # A tibble: 1 × 4
##   color quartile_25 quartile_75   IQR
##   <ord>       <dbl>       <dbl> <dbl>
## 1 J           1860.        7695 5834.

3.2 For the best and worst clarities

diamonds %>% 
  group_by(clarity) %>% 
  filter(clarity == 'I1') %>% 
  summarise(quartile_25 = quantile(price, 0.25),
            quartile_75 = quantile(price, 0.75),
            IQR = quartile_75 - quartile_25)

## # A tibble: 1 × 4
##   clarity quartile_25 quartile_75   IQR
##   <ord>         <dbl>       <dbl> <dbl>
## 1 I1             2080        5161  3081

diamonds %>% 
  group_by(clarity) %>% 
  filter(clarity == 'IF') %>% 
  summarise(quartile_25 = quantile(price, 0.25),
            quartile_75 = quantile(price, 0.75),
            IQR = quartile_75 - quartile_25)

## # A tibble: 1 × 4
##   clarity quartile_25 quartile_75   IQR
##   <ord>         <dbl>       <dbl> <dbl>
## 1 IF              895       2388. 1494.

3.3 For the best and worst cuts

diamonds %>% 
  group_by(cut) %>% 
  filter(cut == 'Fair') %>% 
  summarise(quartile_25 = quantile(price, 0.25),
            quartile_75 = quantile(price, 0.75),
            IQR = quartile_75 - quartile_25)

## # A tibble: 1 × 4
##   cut   quartile_25 quartile_75   IQR
##   <ord>       <dbl>       <dbl> <dbl>
## 1 Fair        2050.       5206. 3155.

diamonds %>% 
  group_by(cut) %>% 
  filter(cut == 'Ideal') %>% 
  summarise(quartile_25 = quantile(price, 0.25),
            quartile_75 = quantile(price, 0.75),
            IQR = quartile_75 - quartile_25)

## # A tibble: 1 × 4
##   cut   quartile_25 quartile_75   IQR
##   <ord>       <dbl>       <dbl> <dbl>
## 1 Ideal         878       4678. 3800.

4. Distribution of the weight (carat) of diamonds

4.1 Frequency polygon

ggplot(diamonds, aes(x=carat)) +
  geom_freqpoly(binwidth=0.1) +
  scale_x_continuous() +
  scale_y_continuous() +
  labs(title='Frequency of diamonds weight',
       subsitle='across colors',
       x='Carat',
       y='Count') +
  theme_fivethirtyeight() +
  theme(axis.title = element_text(), axis.text.x = element_text())

4.2 Frequency polygon (in more depth)

ggplot(diamonds, aes(x=carat)) +
  geom_freqpoly(binwidth=0.05) +
  scale_x_continuous(breaks=c(0.1, 0.5, 0.8, 1.2, 1.5, 2.0, 3.0, 5.0), expand = c(0,0)) +
  scale_y_continuous(expand=c(0,0)) +
  geom_vline(xintercept=c(0.2, 0.4, 0.7, 0.79, 1.04, 5.01), color='#7daa8e', linetype='longdash') +
  labs(title='Frequency of diamonds weight',
       subtitle='Green vlines - summary(diamonds$carat)',
       x='Carat',
       y='Count') +
  theme_fivethirtyeight() +
  theme(axis.title = element_text(), axis.text.x = element_text()) +
  theme(plot.subtitle = element_text(size=10))

summary(diamonds$carat)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.2000  0.4000  0.7000  0.7979  1.0400  5.0100

5. Correlation of price and x, y and z

5.1 Plots

5.1.1 Scatter plot of price vs x

ggplot(diamonds, aes(x=x, y=price)) +
  geom_point(alpha=.05) +
  coord_cartesian(xlim=c(3.5, 11))+
  labs(title='Price vs X') +
  scale_y_continuous(breaks=seq(0, 20000, 2000), label=dollar)

5.1.2 Scatter plot of price vs y

ggplot(diamonds, aes(x=y, y=price)) +
  geom_point(alpha=.05) +
  coord_cartesian(xlim=c(3.5, 11))+
  labs(title='Price vs Y') +
  scale_y_continuous(breaks=seq(0, 20000, 2000), label=dollar)

5.1.3 Scatter plot of price vs z

ggplot(diamonds, aes(x=z, y=price)) +
  geom_point(alpha=.05) +
  coord_cartesian(xlim=c(1, 7))+
  labs(title='Price vs Z') +
  scale_y_continuous(breaks=seq(0, 20000, 2000), label=dollar)

Note: Data appears to have an artificial barrier at $19 000

5.2 Correlation measures

# For the p-value, a typical threshold is 0.05, anything smaller counts as statistically significant

# For price vs X
with(diamonds, cor.test(price, x))

## 
##  Pearson's product-moment correlation
## 
## data:  price and x
## t = 440.16, df = 53938, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.8825835 0.8862594
## sample estimates:
##       cor 
## 0.8844352

# For price vs Y
with(diamonds, cor.test(price, y))

## 
##  Pearson's product-moment correlation
## 
## data:  price and y
## t = 401.14, df = 53938, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.8632867 0.8675241
## sample estimates:
##       cor 
## 0.8654209

# For price vs Z
with(diamonds, cor.test(price, z))

## 
##  Pearson's product-moment correlation
## 
## data:  price and z
## t = 393.6, df = 53938, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.8590541 0.8634131
## sample estimates:
##       cor 
## 0.8612494

Note: There is a strong correlation between the price and the x, y and z values

6. Depth

6.1 Scatter Plot of price vs depth

ggplot(diamonds, aes(x=depth, y=price)) +
  geom_point(alpha=.05) +
  coord_cartesian(xlim=c(40, 80))+
  labs(title='Price vs Depth') +
  scale_y_continuous(breaks=seq(0, 20000, 2000), label=dollar)

6.2 Add transparency and labels every 2 units

ggplot(diamonds, aes(x=depth, y=price)) +
  labs(title='Price vs Depth') +
  geom_point(alpha=1/100) +
  scale_x_continuous(breaks=seq(min(diamonds$depth), max(diamonds$depth), 2), labels=seq(min(diamonds$depth), max(diamonds$depth), 2))

with(diamonds,  cor.test(price, depth))

## 
##  Pearson's product-moment correlation
## 
## data:  price and depth
## t = -2.473, df = 53938, p-value = 0.0134
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.019084756 -0.002208537
## sample estimates:
##        cor 
## -0.0106474

Note: Depth does not influence the price of diamonds. That is visible in the plots and also because p-value = 0.0134 and cor = -0.0106474.

7. Price vs Volume

Note: this is a rough approximation of a diamond’s volume

diamonds_v <- diamonds %>% 
  mutate(volume=x*y*z)

diamonds_v2 <- diamonds_v %>% 
  filter(volume != 0, 
         volume <= 800)

7.1 Scatter Plot of price vs volume

ggplot(diamonds_v, aes(x=volume, y=price)) +
  geom_point() +
  labs(title='Price vs Volume') +
  theme_fivethirtyeight() +
  theme(axis.title = element_text())

Notes:
Volumes ranging from 0-500 hold the huge majority of diamonds, whatever the price
Volumes ranging from 1000-3000 seem to be nonexistent
3 outliers 1 of which expands the plot(horizontaly) by a very significant size
Diamonds with volumes near 0 exist

7.2 Correlation of price and volume

Note: Excluding diamonds with volume of 0 or greater than 800

with(subset(diamonds_v, !(volume == 0 | volume >= 800)), cor.test(price, volume))

## 
##  Pearson's product-moment correlation
## 
## data:  price and volume
## t = 559.19, df = 53915, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.9222944 0.9247772
## sample estimates:
##       cor 
## 0.9235455

7.3 Scatter Plot of price vs volume (omitted top 1%)

ggplot(diamonds_v2, aes(x=volume, y=price)) +
  geom_point(alpha=.05) +
  geom_smooth(method='lm') +
  labs(title='Price vs Volume',
       subtitle='Omitted top 1%') +
  scale_y_continuous(limits=c(0, 20000)) +
  theme_fivethirtyeight() +
  theme(axis.title = element_text()) +
  theme(plot.subtitle = element_text(size=10))

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 37 rows containing missing values (geom_smooth).

8. Information on diamonds by clarity

I created a new data frame with the following columns:
mean_price
median_price
min_price
max_price
n (number of diamonds on each level of clarity)

diamondsByClarity <- diamonds %>% 
  group_by(clarity) %>% 
  summarise(mean_price = mean(price),
            median_price = median(price),
            min_price = min(price),
            max_price = max(price),
            count = n()) %>% 
  arrange(clarity)

8.1 Mean price vs color and clarity

8.1.1 Create summary dataframes

# By clarity and color separately
diamonds_mp_cla <- diamonds %>% 
  group_by(clarity) %>% 
  summarise(mean_price = mean(price))

diamonds_mp_col <- diamonds %>% 
  group_by(color) %>% 
  summarise(mean_price = mean(price))

8.1.2 Plot the summary dataframes

# Using library(gridExtra)
plt1 <- ggplot(diamonds_mp_cla, aes(x=clarity, y=mean_price, fill=clarity)) +
  geom_bar(stat = "identity", color='black') +
  guides(fill=guide_legend(ncol=2))

plt2 <- ggplot(diamonds_mp_col, aes(x=color, y=mean_price, fill=color)) +
  geom_bar(stat = "identity", color='black') +
  guides(fill=guide_legend(ncol=2))

grid.arrange(plt1, plt2)

Notes:
Upward trend in average price as color goes from D to J;
Downward trend in average price as clarity goes from I1 to IF

9. Aditional plots

9.1 Price vs table by color

ggplot(diamonds, aes(x=table, y=price, color=cut)) +
  geom_point(size=2.5) +
  labs(title='Price vs Table',
       x='table',
       y='price') +
  scale_x_continuous(breaks=seq(50, 80, 2),
                     limits=c(50, 80)) +
  scale_color_brewer(palette = 'RdGy') +
  theme_fivethirtyeight() +
  theme(axis.title = element_text(), axis.text.x = element_text())

## Warning: Removed 5 rows containing missing values (geom_point).

9.2 Price vs volume by clarity

Notes:
Top 1% of diamond volumes omitted
Again, this is a rough approximation of a diamond’s volume

# Add volume column
diamonds <- diamonds %>% 
  mutate(volume = x * y * z) 

ggplot(subset(diamonds, volume <= quantile(volume, 0.99) & volume > 0), aes(x=volume, y=price, color=clarity)) +
  geom_point(size=2.5) +
  scale_y_log10(labels=dollar, 
                breaks=c(0, 1000, 10000)) +
  scale_color_brewer(palette = 'BrBG') +
  theme_minimal()

9.3 Price/carat ratio of diamonds

Notes:
Variable x is assigned to cut
Points are colored by diamond color
Plot is faceted by clarity

ggplot(diamonds, aes(x=cut, y=price/carat, color=color)) +
  geom_jitter() +
  facet_wrap(~clarity) +
  scale_color_brewer(palette = 'BrBG')

diamonds_analysis

João Calheiros

28/12/2021